In semiconductor manufacturing technologies, one important process involves localization and measurement of surface mount component (“SMC”) objects for industrial inspection, and more particularly, for pick-and-place applications. Accurate and efficient algorithms are required to achieve reliable inspection tasks, which should be performed robustly under loose image conditions in terms of object appearance, size, pose range, illumination, noise and the like. Such algorithms promise increased flexibility and reduced cost for machine vision systems.
As the geometries of SMC objects become smaller with advances in semiconductor manufacturing capabilities, the pick-and-place systems call for highly demanding inspection of SMC objects in miniature scale. However, the performance of an algorithm is intrinsically bound with the image quality and conditions. Maintaining unlimited image condition quality while also maintaining robustness is not practical. Thus, investigation of how small objects can be correctly localized and measured under the requirements of pick-and-place systems is an area of current interest.
In general, object localization is defined as a problem of finding the pose transformation (i.e., translation, rotation and scaling) between observed and reference data sets lying in two different spaces. Given an evaluation function for the distance and/or energy between two data sets, the pose transformation may be obtained by determining the parameters that result in a minimal value of the evaluation function. In addition to localization, object measurement is performed to find the dimensions between a pair of points and/or lines on the object. For example, the width and the height of an object are desirable measurements. Once the object is detected or localized, object measurement can usually be performed based on line-fitting algorithms.
Numerous object localization and measurement algorithms have been implemented for general industrial inspection applications. These range from correlation-based template matching to generalized Hough transforms and contour-based matching.
However, most of the existing techniques are not suitable for the pick-and-place applications that require inspecting miniature SMC objects. The techniques are generally unsuitable because they are either computationally intensive, have difficulties establishing correspondences between reference data and object data, and/or require extensive and highly reliable data to run in practice. Another problem for measuring miniature SMC objects is that their appearance can vary due to differences in manufacturing processes and the variations in local lighting conditions. Conventional methods were often based on learning-from-example approaches, which become impractical for these applications. In addition, the pick-and-place systems preferably use as few sets of reference data as possible due to limited memory storage.
Therefore, it is desirable that one set of reference data be used for inspecting the same types of objects regardless of their appearance variations. These and other requirements exclude many existing algorithms from consideration for measuring miniature SMC objects.